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Cynthia Bennett

Publications and source records attributed to Cynthia Bennett.

4 recordsLinked to original sources
arXiv2025

Access InContext: Futuring Accessible Prototyping Tools and Methods

The popularity of accessibility research has grown recently, improving digital inclusion for people with disabilities. However, researchers, including those who have disabilities, have attempted to include people with disabilities in all aspects of design, and they have identified a myriad of practical accessibility barriers posed by tools and methods leveraged by human-computer interaction (HCI) researchers during prototyping. To build a more inclusive technological landscape, we must question the effectiveness of existing prototyping tools and methods, repurpose/retrofit existing resources, and build new tools and methods to support the participation of both researchers and people with disabilities within the prototyping design process of novel technologies. This full-day workshop at CHI 2025 will provide a platform for HCI researchers, designers, and practitioners to discuss barriers and opportunities for creating accessible prototyping and promote hands-on ideation and fabrication exercises aimed at futuring accessible prototyping.

cs.HC↗
arXiv2024

Investigating Use Cases of AI-Powered Scene Description Applications for Blind and Low Vision People

"Scene description" applications that describe visual content in a photo are useful daily tools for blind and low vision (BLV) people. Researchers have studied their use, but they have only explored those that leverage remote sighted assistants; little is known about applications that use AI to generate their descriptions. Thus, to investigate their use cases, we conducted a two-week diary study where 16 BLV participants used an AI-powered scene description application we designed. Through their diary entries and follow-up interviews, users shared their information goals and assessments of the visual descriptions they received. We analyzed the entries and found frequent use cases, such as identifying visual features of known objects, and surprising ones, such as avoiding contact with dangerous objects. We also found users scored the descriptions relatively low on average, 2.76 out of 5 (SD=1.49) for satisfaction and 2.43 out of 4 (SD=1.16) for trust, showing that descriptions still need significant improvements to deliver satisfying and trustworthy experiences. We discuss future opportunities for AI as it becomes a more powerful accessibility tool for BLV users.

cs.HC↗
arXiv2022

Context Matters for Image Descriptions for Accessibility: Challenges for Referenceless Evaluation Metrics

Few images on the Web receive alt-text descriptions that would make them accessible to blind and low vision (BLV) users. Image-based NLG systems have progressed to the point where they can begin to address this persistent societal problem, but these systems will not be fully successful unless we evaluate them on metrics that guide their development correctly. Here, we argue against current referenceless metrics -- those that don't rely on human-generated ground-truth descriptions -- on the grounds that they do not align with the needs of BLV users. The fundamental shortcoming of these metrics is that they do not take context into account, whereas contextual information is highly valued by BLV users. To substantiate these claims, we present a study with BLV participants who rated descriptions along a variety of dimensions. An in-depth analysis reveals that the lack of context-awareness makes current referenceless metrics inadequate for advancing image accessibility. As a proof-of-concept, we provide a contextual version of the referenceless metric CLIPScore which begins to address the disconnect to the BLV data. An accessible HTML version of this paper is available at https://elisakreiss.github.io/contextual-description-evaluation/paper/reflessmetrics.html

cs.CL↗